Data Engineer

At Riot, Data Engineers build the trusted data foundations that help teams make better decisions and create better experiences for players. As a Principal Data Engineer on Teamfight Tactics CoreTech, you will own the technical direction and long-term health of TFT’s data ecosystem, from telemetry and schemas to govern data products, metrics, pipelines, and production operations.

You will partner closely with Game Engineering, Design, Product, Insights, and Data Science from feature conception through launch and live operations. You will translate player and product needs into scalable, reliable, and cost-efficient data solutions, while establishing the standards and operating practices that allow TFT teams to move quickly without compromising data trust.

This is a Principal-level individual contributor role. You will lead ambiguous, multi-team initiatives; make architecture and prioritization tradeoffs; mentor engineers and data practitioners; and influence data-engineering standards across TFT and Riot. You will remain hands-on where it creates leverage, while raising the effectiveness of the broader team through technical direction, coaching, and durable systems. 

Responsibilities:

• Own the technical strategy, architecture, and roadmap for TFT’s data ecosystem in partnership with TFT and Riot Data Engineering leadership.
• Design, build, and evolve scalable batch and near-real-time data systems supporting telemetry, player behavior, game operations, experimentation, insights, and player-facing features.
• Establish and maintain data contracts across Data Engineering, Insights, Game Engineering, and Product, including event schemas, metric definitions, and accuracy expectations.
• Partner with game teams early in feature development to design instrumentation, validate payloads, and make telemetry a tested part of feature readiness and launch criteria.
• Own the reliability and operational health of critical TFT data products, including freshness, availability, accuracy, observability, alerting, runbooks, incident response, and root-cause analysis.
• Lead data modeling and warehouse architecture across TFT, creating durable, well-documented sources of truth that serve multiple teams and use cases.
• Establish standards for data governance, lineage, documentation, access, retention, lifecycle management, and compliance with applicable privacy requirements.
• Reduce ambiguity by clarifying requirements, identifying risks, evaluating alternatives, and communicating tradeoffs across delivery speed, reliability, cost, and complexity.
• Improve engineering velocity through automation, self-service tooling, reusable patterns, and pragmatic development and review processes.